/
univariate_functions.h
802 lines (725 loc) · 27.1 KB
/
univariate_functions.h
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/**
* \file IMP/isd/univariate_functions.h
* \brief Classes for general functions
*
* Copyright 2007-2022 IMP Inventors. All rights reserved.
*/
#ifndef IMPISD_UNIVARIATE_FUNCTIONS_H
#define IMPISD_UNIVARIATE_FUNCTIONS_H
#include <IMP/isd/isd_config.h>
#include <IMP/Particle.h>
#include <IMP/isd/Nuisance.h>
#include <IMP/isd/Scale.h>
#include <IMP/isd/Switching.h>
#include <IMP/Object.h>
#include <Eigen/Dense>
#define IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM 1e-7
IMPISD_BEGIN_NAMESPACE
//! Base class for functions of one variable
class IMPISDEXPORT UnivariateFunction : public Object {
public:
UnivariateFunction(std::string str) : Object(str) {}
//! evaluate the function at a certain point
virtual Floats operator()(const Floats& x) const = 0;
//! evaluate the function at a list of points
virtual Eigen::VectorXd operator()(const IMP::FloatsList& xlist)
const = 0;
//! used for testing only
virtual FloatsList operator()(const IMP::FloatsList& xlist,
bool stupid) const = 0;
//! return true if internal parameters have changed.
virtual bool has_changed() const = 0;
//! update internal parameters
virtual void update() = 0;
//! update derivatives of particles
/* add to each particle the derivative of the function
* times the weight of the DA.
*/
virtual void add_to_derivatives(const Floats& x,
DerivativeAccumulator& accum) const = 0;
//! update derivatives of particles
/* add to the given particle the specified derivative
* guarantees that the particle_no (starting at 0) matches with
* the columns of get_derivative_matrix.
*/
virtual void add_to_particle_derivative(unsigned particle_no, double value,
DerivativeAccumulator& accum)
const = 0;
//! return derivative vector
/* This function returns a column from the matrix m:
* m_ij = d(func(xlist[i]))/dparticle_j
* The matrix has N rows and M columns
* where N = xlist.size() and M is the number of particles
* associated to this function.
*/
virtual Eigen::VectorXd get_derivative_vector(
unsigned particle_no, const FloatsList& xlist) const = 0;
//! for testing purposes
virtual FloatsList get_derivative_matrix(const FloatsList& xlist,
bool stupid) const = 0;
//! return second derivative vector
virtual Eigen::VectorXd get_second_derivative_vector(
unsigned particle_a, unsigned particle_b,
const FloatsList& xlist) const = 0;
//! for testing purposes
virtual FloatsList get_second_derivative_vector(unsigned particle_a,
unsigned particle_b,
const FloatsList& xlist,
bool stupid) const = 0;
//! returns the number of input dimensions
virtual unsigned get_ndims_x() const = 0;
//! returns the number of output dimensions
virtual unsigned get_ndims_y() const = 0;
//! returns the number of particles that this function uses
virtual unsigned get_number_of_particles() const = 0;
//! returns true if the particle whose index is provided is optimized
virtual bool get_particle_is_optimized(unsigned particle_no) const = 0;
//! returns the number of particles that are optimized
virtual unsigned get_number_of_optimized_particles() const = 0;
//! particle manipulation
virtual ModelObjectsTemp get_inputs() const = 0;
IMP_REF_COUNTED_DESTRUCTOR(UnivariateFunction);
};
//
//! Linear one-dimensional function
/* f(x) = a*x + b, where a,b are ISD nuisances, and f(x) and x are doubles.
*/
class IMPISDEXPORT Linear1DFunction : public UnivariateFunction {
public:
Linear1DFunction(Particle* a, Particle* b)
: UnivariateFunction("Linear1DFunction %1%"), a_(a), b_(b) {
IMP_LOG_TERSE("Linear1DFunction: constructor" << std::endl);
a_val_ = Nuisance(a).get_nuisance();
b_val_ = Nuisance(b).get_nuisance();
update();
}
bool has_changed() const override {
double tmpa = Nuisance(a_).get_nuisance();
double tmpb = Nuisance(b_).get_nuisance();
if ((std::abs(tmpa - a_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM) ||
(std::abs(tmpb - b_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM)) {
IMP_LOG_TERSE("Linear1DFunction: has_changed():");
IMP_LOG_TERSE("true" << std::endl);
return true;
} else {
return false;
}
}
void update() override {
a_val_ = Nuisance(a_).get_nuisance();
b_val_ = Nuisance(b_).get_nuisance();
IMP_LOG_TERSE("Linear1DFunction: update() a:= " << a_val_ << " b:="
<< b_val_ << std::endl);
}
Floats operator()(const Floats& x) const override {
IMP_USAGE_CHECK(x.size() == 1, "expecting a 1-D vector");
Floats ret(1, a_val_ * x[0] + b_val_);
return ret;
}
Eigen::VectorXd operator()(const FloatsList& xlist) const override {
unsigned M = xlist.size();
Eigen::VectorXd retlist(M);
for (unsigned i = 0; i < M; i++) {
IMP_USAGE_CHECK(xlist[i].size() == 1, "expecting a 1-D vector");
retlist(i) = a_val_ * xlist[i][0] + b_val_;
}
return retlist;
}
FloatsList operator()(const FloatsList& xlist, bool) const override {
Eigen::VectorXd vec((*this)(xlist));
FloatsList ret;
for (unsigned i = 0; i < xlist.size(); i++)
ret.push_back(Floats(1, vec(i)));
return ret;
}
void add_to_derivatives(const Floats& x,
DerivativeAccumulator& accum) const override {
// d[f(x)]/da = x
Nuisance(a_).add_to_nuisance_derivative(x[0], accum);
// d[f(x)]/db = 1
Nuisance(b_).add_to_nuisance_derivative(1, accum);
}
void add_to_particle_derivative(unsigned particle_no, double value,
DerivativeAccumulator& accum) const override {
switch (particle_no) {
case 0:
Nuisance(a_).add_to_nuisance_derivative(value, accum);
break;
case 1:
Nuisance(b_).add_to_nuisance_derivative(value, accum);
break;
default:
IMP_THROW("Invalid particle number", ModelException);
}
}
Eigen::VectorXd get_derivative_vector(unsigned particle_no,
const FloatsList& xlist) const override {
unsigned N = xlist.size();
Eigen::VectorXd ret(N);
switch (particle_no) {
case 0: // a
for (unsigned i = 0; i < N; i++) ret(i) = xlist[i][0];
break;
case 1: // b
ret.setOnes();
break;
default:
IMP_THROW("Invalid particle number", ModelException);
}
return ret;
}
FloatsList get_derivative_matrix(
const FloatsList& xlist, bool) const override {
Eigen::MatrixXd mat(xlist.size(), 2);
mat.col(0) = get_derivative_vector(0, xlist);
mat.col(1) = get_derivative_vector(1, xlist);
FloatsList ret;
for (int i = 0; i < mat.rows(); i++) {
Floats line;
for (int j = 0; j < mat.cols(); j++) line.push_back(mat(i, j));
ret.push_back(line);
}
return ret;
}
Eigen::VectorXd get_second_derivative_vector(
unsigned, unsigned, const FloatsList& xlist) const override {
// The Hessian is zero for all particles.
unsigned N = xlist.size();
Eigen::VectorXd H(Eigen::VectorXd::Zero(N));
return H;
}
FloatsList get_second_derivative_vector(
unsigned particle_a, unsigned particle_b,
const FloatsList& xlist, bool) const override {
Eigen::VectorXd mat(
get_second_derivative_vector(particle_a, particle_b, xlist));
FloatsList ret;
for (int i = 0; i < mat.rows(); i++) {
Floats line;
for (int j = 0; j < mat.cols(); j++) line.push_back(mat(i, j));
ret.push_back(line);
}
return ret;
}
unsigned get_ndims_x() const override { return 1; }
unsigned get_ndims_y() const override { return 1; }
unsigned get_number_of_particles() const override { return 2; }
bool get_particle_is_optimized(unsigned particle_no) const override {
switch (particle_no) {
case 0: // a
return Nuisance(a_).get_nuisance_is_optimized();
case 1: // b
return Nuisance(b_).get_nuisance_is_optimized();
default:
IMP_THROW("Invalid particle number", ModelException);
}
}
unsigned get_number_of_optimized_particles() const override {
unsigned count = 0;
if (Nuisance(a_).get_nuisance_is_optimized()) count++;
if (Nuisance(b_).get_nuisance_is_optimized()) count++;
return count;
}
ModelObjectsTemp get_inputs() const override {
ModelObjectsTemp ret;
ret.push_back(a_);
ret.push_back(b_);
return ret;
}
/*IMP_OBJECT_INLINE(Linear1DFunction, out << "y = " << a_val_
<< " * x + " << b_val_ << std::endl, {});*/
IMP_OBJECT_METHODS(Linear1DFunction);
private:
Pointer<Particle> a_, b_;
double a_val_, b_val_;
};
//! 1D mean function for SAS data
/* Generalized Guinier-Porod model (Hammouda, J. Appl. Cryst., 2010, eq 3 & 4
* to which a constant offset is added)
* I(q) = A + G/q^s exp(-(q.Rg)^2/(3-s)) for q <= q1
* I(q) = A + D/q^d for q > q1
* q1 = 1/Rg * ((d-s)(3-s)/2)^(1/2)
* D = G exp(-(q1.Rg)^2/(3-s)) q1^(d-s)
* only valid for q>0 Rg>0 0<s<d s<3 G>0
* s=0 in the globular case.
* G, Rg, d, s and A are particles (ISD Scales).
*/
class IMPISDEXPORT GeneralizedGuinierPorodFunction : public UnivariateFunction {
public:
GeneralizedGuinierPorodFunction(Particle* G, Particle* Rg,
Particle* d, Particle* s,
Particle* A)
: UnivariateFunction("GeneralizedGuinierPorodFunction %1%"),
G_(G),
Rg_(Rg),
d_(d),
s_(s),
A_(A) {
IMP_LOG_TERSE("GeneralizedGuinierPorodFunction: constructor" << std::endl);
update();
}
bool has_changed() const override {
double tmpG = Scale(G_).get_scale();
double tmpRg = Scale(Rg_).get_scale();
double tmpd = Scale(d_).get_scale();
double tmps = Scale(s_).get_scale();
double tmpA = Nuisance(A_).get_nuisance();
if ((std::abs(tmpG - G_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM) ||
(std::abs(tmpRg - Rg_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM) ||
(std::abs(tmpd - d_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM) ||
(std::abs(tmps - s_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM) ||
(std::abs(tmpA - A_val_) > IMP_ISD_UNIVARIATE_FUNCTIONS_MINIMUM)) {
IMP_LOG_TERSE("GeneralizedGuinierPorodFunction: has_changed():");
IMP_LOG_TERSE("true" << std::endl);
return true;
} else {
return false;
}
}
void update() override {
G_val_ = Scale(G_).get_scale();
Rg_val_ = Scale(Rg_).get_scale();
d_val_ = Scale(d_).get_scale();
s_val_ = Scale(s_).get_scale();
if (d_val_ == s_val_) {
IMP_LOG_TERSE("Warning: d==s !" << std::endl);
if (s_val_ > 0.001) {
s_val_ -= 0.001;
} else {
d_val_ += 0.001;
}
// IMP_THROW("d == s ! ", ModelException);
}
A_val_ = Nuisance(A_).get_nuisance();
q1_param_ = std::sqrt((d_val_ - s_val_) * (3 - s_val_) / 2.);
D_param_ = G_val_ * std::exp(-IMP::square(q1_param_) / (3 - s_val_));
q1_param_ = q1_param_ / Rg_val_;
D_param_ *= std::pow(q1_param_, d_val_ - s_val_);
IMP_LOG_TERSE("GeneralizedGuinierPorodFunction: update() G:= "
<< G_val_ << " Rg:=" << Rg_val_ << " d:=" << d_val_
<< " s:=" << s_val_ << " A:=" << A_val_ << " Q1.Rg ="
<< q1_param_ * Rg_val_ << " D =" << D_param_ << std::endl);
/*std::cout << "cpp"
<< " 3:Q1 " << q1_param_
<< " 5:D " << D_param_
<< " 7:G " << G_val_
<< " 9:Rg " << Rg_val_
<< " 11:d " << d_val_
<< " 13:s " << s_val_
<< " 15:val " << get_value(0.1)
<<std::endl;*/
}
Floats operator()(const Floats& x) const override {
IMP_USAGE_CHECK(x.size() == 1, "expecting a 1-D vector");
Floats ret(1, get_value(x[0]));
return ret;
}
Eigen::VectorXd operator()(const FloatsList& xlist) const override {
unsigned M = xlist.size();
Eigen::VectorXd retlist(M);
for (unsigned i = 0; i < M; i++) {
IMP_USAGE_CHECK(xlist[i].size() == 1, "expecting a 1-D vector");
retlist(i) = get_value(xlist[i][0]);
}
return retlist;
}
FloatsList operator()(const FloatsList& xlist, bool) const override {
Eigen::VectorXd vec((*this)(xlist));
FloatsList ret;
for (unsigned i = 0; i < xlist.size(); i++)
ret.push_back(Floats(1, vec(i)));
return ret;
}
void add_to_derivatives(
const Floats& x, DerivativeAccumulator& accum) const override {
double qval = x[0];
double value = get_value(qval) - A_val_;
double deriv;
// d[f(x)+A]/dG = f(x)/G
deriv = value / G_val_;
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for G is nan.");
Scale(G_).add_to_nuisance_derivative(deriv, accum);
if (qval <= q1_param_) {
// d[f(x)]/dRg = - f(x) * 2 q^2 Rg / (3-s)
deriv = -value * 2 * IMP::square(qval) * Rg_val_ / (3 - s_val_);
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for Rg is nan.");
Scale(Rg_).add_to_nuisance_derivative(deriv, accum);
// d[f(x)]/dd = 0
//
// d[f(x)]/ds = - f(x) * ( (q Rg / (3-s))^2 + log(q) )
deriv = -value *
(IMP::square((qval * Rg_val_) / (3 - s_val_)) + std::log(qval));
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for s is nan.");
Scale(s_).add_to_nuisance_derivative(deriv, accum);
} else {
// d[f(x)]/dRg = f(x) * (s-d)/Rg
deriv = value * (s_val_ - d_val_) / Rg_val_;
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for Rg is nan.");
Scale(Rg_).add_to_nuisance_derivative(deriv, accum);
// d[f(x)]/dd = f(x) * log(q1/q)
deriv = value * std::log(q1_param_ / qval);
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for d is nan.");
Scale(d_).add_to_nuisance_derivative(deriv, accum);
// d[f(x)]/ds = - f(x) * ( (d-s)/(2(3-s)) + log(q1) )
deriv = -value *
((d_val_ - s_val_) / (2 * (3 - s_val_)) + std::log(q1_param_));
IMP_INTERNAL_CHECK(!IMP::isnan(deriv), "derivative for d is nan.");
Scale(s_).add_to_nuisance_derivative(deriv, accum);
}
// d[f(x)+A]/dA = 1
deriv = 1;
Nuisance(A_).add_to_nuisance_derivative(deriv, accum);
}
void add_to_particle_derivative(unsigned particle_no, double value,
DerivativeAccumulator& accum) const override {
switch (particle_no) {
case 0:
IMP_INTERNAL_CHECK(!IMP::isnan(value), "derivative for G is nan.");
Scale(G_).add_to_scale_derivative(value, accum);
break;
case 1:
IMP_INTERNAL_CHECK(!IMP::isnan(value), "derivative for Rg is nan.");
Scale(Rg_).add_to_scale_derivative(value, accum);
break;
case 2:
IMP_INTERNAL_CHECK(!IMP::isnan(value), "derivative for d is nan.");
Scale(d_).add_to_scale_derivative(value, accum);
break;
case 3:
IMP_INTERNAL_CHECK(!IMP::isnan(value), "derivative for s is nan.");
Scale(s_).add_to_scale_derivative(value, accum);
break;
case 4:
IMP_INTERNAL_CHECK(!IMP::isnan(value), "derivative for A is nan.");
Nuisance(A_).add_to_nuisance_derivative(value, accum);
break;
default:
IMP_THROW("Invalid particle number", ModelException);
}
}
Eigen::VectorXd get_derivative_vector(unsigned particle_no,
const FloatsList& xlist) const override {
unsigned N = xlist.size();
Eigen::VectorXd ret(N);
switch (particle_no) {
case 0: // G
// d[f(x)]/dG = f(x)/G
ret = ((*this)(xlist) - Eigen::VectorXd::Constant(N, A_val_)) /
G_val_;
break;
case 1: // Rg
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d[f(x)]/dRg = - f(x) * 2 q^2 Rg / (3-s)
ret(i) = -(get_value(qval) - A_val_) * 2 * IMP::square(qval) *
Rg_val_ / (3 - s_val_);
} else {
// d[f(x)]/dRg = f(x) * (s-d)/Rg
ret(i) = (get_value(qval) - A_val_) * (s_val_ - d_val_) / Rg_val_;
}
}
break;
case 2: // d
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d[f(x)]/dd = 0
ret(i) = 0;
} else {
// d[f(x)]/dd = f(x) * log(q1/q)
ret(i) = (get_value(qval) - A_val_) * std::log(q1_param_ / qval);
}
}
break;
case 3: // s
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d[f(x)]/ds = - f(x)
// * (q^2 Rg^2 + (3-s)^2 log(q)) / (3-s)^2
ret(i) =
-(get_value(qval) - A_val_) *
(IMP::square((qval * Rg_val_) / (3 - s_val_)) + std::log(qval));
} else {
// d[f(x)]/ds = - f(x) * ( (d-s)/(2(3-s)) + log(q1) )
ret(i) =
-(get_value(qval) - A_val_) *
((d_val_ - s_val_) / (2 * (3 - s_val_)) + std::log(q1_param_));
}
}
break;
case 4: // A
ret = Eigen::VectorXd::Constant(N, 1);
break;
default:
IMP_THROW("Invalid particle number", ModelException);
}
return ret;
}
FloatsList get_derivative_matrix(
const FloatsList& xlist, bool) const override {
Eigen::MatrixXd mat(xlist.size(), 5);
mat.col(0) = get_derivative_vector(0, xlist);
mat.col(1) = get_derivative_vector(1, xlist);
mat.col(2) = get_derivative_vector(2, xlist);
mat.col(3) = get_derivative_vector(3, xlist);
mat.col(4) = get_derivative_vector(4, xlist);
FloatsList ret;
for (int i = 0; i < mat.rows(); i++) {
Floats line;
for (int j = 0; j < mat.cols(); j++) line.push_back(mat(i, j));
ret.push_back(line);
}
return ret;
}
Eigen::VectorXd get_second_derivative_vector(
unsigned particle_a, unsigned particle_b,
const FloatsList& xlist) const override {
if (particle_a >= 5) IMP_THROW("Invalid particle 1 number", ModelException);
if (particle_b >= 5) IMP_THROW("Invalid particle 2 number", ModelException);
unsigned N = xlist.size();
// hessian involving A is always 0
if (particle_a == 4 || particle_b == 4) return Eigen::VectorXd::Zero(N);
unsigned pa = std::min(particle_a, particle_b);
unsigned pb = std::max(particle_a, particle_b);
Eigen::VectorXd ret(N);
switch (pa) {
case 0: // G
switch (pb) {
case 0: // G
// d2f/dG2 = 0
ret.noalias() = Eigen::VectorXd::Zero(N);
break;
case 1: // Rg
// d2f/dGdRg = df/dRg * 1/G
ret.noalias() = get_derivative_vector(1, xlist) / G_val_;
break;
case 2: // d
// d2f/dGdd = df/dd * 1/G
ret.noalias() = get_derivative_vector(2, xlist) / G_val_;
break;
case 3: // s
// d2f/dGds = df/ds * 1/G
ret.noalias() = get_derivative_vector(3, xlist) / G_val_;
break;
default:
IMP_THROW("Invalid particle 2 number", ModelException);
break;
}
break;
case 1: // Rg
switch (pb) {
case 1: // Rg
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d2[f(x)]/dRg2 =
// f(x) * (2 q^2 / (3-s))
// * (2 q^2 Rg^2 / (3-s) - 1)
ret(i) = (get_value(qval) - A_val_) * 2 * IMP::square(qval) /
(3 - s_val_) *
(2 * IMP::square(qval * Rg_val_) / (3 - s_val_) - 1);
} else {
// d2[f(x)]/dRg2=f(x) * (d-s)/Rg^2 * (d-s+1)
ret(i) = (get_value(qval) - A_val_) * (d_val_ - s_val_) /
IMP::square(Rg_val_) * (d_val_ - s_val_ + 1);
}
}
break;
case 2: // d
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d2[f(x)]/dddRg = 0
ret(i) = 0;
} else {
// d2[f(x)]/dddRg = -f(x)/Rg
// - (d-s)/Rg *df(x)/dd
double val = (get_value(qval) - A_val_);
ret(i) = -val / Rg_val_ - (val * std::log(q1_param_ / qval) *
(d_val_ - s_val_) / Rg_val_);
}
}
break;
case 3: // s
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
double val = (get_value(qval) - A_val_);
if (qval <= q1_param_) {
// d2[f(x)]/dsdRg = -2q^2Rg/(3-s)
// * (df(x)/ds + f(x)/(3-s))
double deriv =
-val * (IMP::square((qval * Rg_val_) / (3 - s_val_)) +
std::log(qval));
ret(i) = -2 * IMP::square(qval) * Rg_val_ / (3 - s_val_) *
(deriv + val / (3 - s_val_));
} else {
// d2[f(x)]/dsdRg =
// 1/Rg * (f(x)- (d-s)*df(x)/ds)
double deriv = -val * ((d_val_ - s_val_) / (2 * (3 - s_val_)) +
std::log(q1_param_));
ret(i) = (val - (d_val_ - s_val_) * deriv) / Rg_val_;
}
}
break;
default:
IMP_THROW("Invalid particle 2 number", ModelException);
break;
}
break;
case 2: // d
switch (pb) {
case 2: // d
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d2[f(x)]/dddd = 0
ret(i) = 0;
} else {
// d2[f(x)]/dddd =
// f(x)*(log(q1/q)^2 + 1/(2*(d-s)))
double val = (get_value(qval) - A_val_);
ret(i) = val * (IMP::square(std::log(q1_param_ / qval)) +
1 / (2 * (d_val_ - s_val_)));
}
}
break;
case 3: // s
{
double lterm =
(d_val_ - s_val_) / (2 * (3 - s_val_)) + std::log(q1_param_);
double rterm = 0.5 * (1 / (3 - s_val_) + 1 / (d_val_ - s_val_));
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
if (qval <= q1_param_) {
// d2[f(x)]/ddds = 0
ret(i) = 0;
} else {
// d2[f(x)]/ddds = log(q1/q)*df(x)/ds
// - (1/(3-s) + 1/(d-s))*f(x)/2
//
double val = (get_value(qval) - A_val_);
ret(i) = -val * (std::log(q1_param_ / qval) * lterm + rterm);
}
}
} break;
default:
IMP_THROW("Invalid particle 2 number", ModelException);
break;
}
break;
case 3: // s
switch (pb) {
case 3: // s
{
double cterm =
IMP::square(0.5 * (d_val_ - s_val_) / (3 - s_val_) +
std::log(q1_param_)) +
0.5 * ((6 - s_val_ - d_val_) / IMP::square(3 - s_val_) +
1. / (d_val_ - s_val_));
for (unsigned i = 0; i < N; i++) {
double qval = xlist[i][0];
double val = (get_value(qval) - A_val_);
if (qval <= q1_param_) {
// d2[f(x)]/dsds = f(x) *
// ( [(qRg)^2/(3-s)^2 + log q ]^2
// - 2(qRg)^2/(3-s)^3 )
double factor = IMP::square((qval * Rg_val_) / (3 - s_val_));
ret(i) = val * (IMP::square(factor + std::log(qval)) -
2 * factor / (3 - s_val_));
} else {
// d2[f(x)]/dsds = f(x)
// * ( [ (d-s)/(2*(3-s)) + log(q1) ]^2
// + 1/2 * [ (6-s-d)/(3-s)^2
// + 1/(d-s) ] )
ret(i) = val * cterm;
}
}
} break;
default:
IMP_THROW("Invalid particle 2 number", ModelException);
break;
}
break;
default:
IMP_THROW("Invalid particle 1 number", ModelException);
break;
}
return ret;
}
FloatsList get_second_derivative_vector(
unsigned particle_a, unsigned particle_b,
const FloatsList& xlist, bool) const override {
Eigen::VectorXd mat(
get_second_derivative_vector(particle_a, particle_b, xlist));
FloatsList ret;
for (int i = 0; i < mat.rows(); i++) {
Floats line;
for (int j = 0; j < mat.cols(); j++) line.push_back(mat(i, j));
ret.push_back(line);
}
return ret;
}
unsigned get_ndims_x() const override { return 1; }
unsigned get_ndims_y() const override { return 1; }
unsigned get_number_of_particles() const override { return 5; }
bool get_particle_is_optimized(unsigned particle_no) const override {
switch (particle_no) {
case 0: // G
return Scale(G_).get_scale_is_optimized();
case 1: // Rg
return Scale(Rg_).get_scale_is_optimized();
case 2: // d
return Scale(d_).get_scale_is_optimized();
case 3: // s
return Scale(s_).get_scale_is_optimized();
case 4: // A
return Nuisance(A_).get_nuisance_is_optimized();
default:
IMP_THROW("Invalid particle number", ModelException);
}
}
unsigned get_number_of_optimized_particles() const override {
unsigned count = 0;
if (Scale(G_).get_scale_is_optimized()) count++;
if (Scale(Rg_).get_scale_is_optimized()) count++;
if (Scale(d_).get_scale_is_optimized()) count++;
if (Scale(s_).get_scale_is_optimized()) count++;
if (Nuisance(A_).get_nuisance_is_optimized()) count++;
return count;
}
ModelObjectsTemp get_inputs() const override {
ModelObjectsTemp ret;
ret.push_back(G_);
ret.push_back(Rg_);
ret.push_back(d_);
ret.push_back(s_);
ret.push_back(A_);
return ret;
}
/*IMP_OBJECT_INLINE(GeneralizedGuinierPorodFunction, out
<< " G = " << G_val_
<< " Rg = " << Rg_val_
<< " d = " << d_val_
<< " s = " << s_val_
<< " A = " << A_val_
<< " Q1.Rg = " << q1_param_*Rg_val_
<< std::endl, {});*/
IMP_OBJECT_METHODS(GeneralizedGuinierPorodFunction);
private:
inline double get_value(double q) const {
double value;
if (q <= q1_param_) {
value = A_val_ + G_val_ / std::pow(q, s_val_) *
std::exp(-IMP::square(q * Rg_val_) / (3 - s_val_));
} else {
value = A_val_ + D_param_ / std::pow(q, d_val_);
}
return value;
}
Pointer<Particle> G_, Rg_, d_, s_, A_;
double G_val_, Rg_val_, d_val_, s_val_, A_val_, q1_param_, D_param_;
};
IMPISD_END_NAMESPACE
#endif /* IMPISD_UNIVARIATE_FUNCTIONS_H */